The hidden Markov models are statistical models used in many real-world applications and
communities. The use of hidden Markov models has become predominant in the last …

Today, daily life is composed of many computing systems, therefore interacting with them in
a natural way makes the communication process more comfortable. Human–Computer …

Control of contemporary, multi-joint prosthetic hands is commonly realized by using
electromyographic signals from the muscles remaining after amputation at the forearm level …

Significance: The performance of traditional approaches to decoding movement intent from
electromyograms (EMGs) and other biological signals commonly degrade over time …

Bio-signals are distinctive factors in the design of human-machine interface, essentially
useful for prosthesis, orthosis, and exoskeletons. Despite the progress in the analysis of …

Background Processing the surface electromyogram (sEMG) to decode movement intent is a
promising approach for natural control of upper extremity prostheses. To this end, this paper …

In the field of noncontact human‐computer interaction, it is of crucial importance to
distinguish different surface electromyography (sEMG) gestures accurately for intelligent …

Abstract Convolutional Neural Networks (CNNs) have been subject to extensive attention in
the pattern recognition literature due to unprecedented performance in tasks of information …

As a novel and non-invasive sensing technology, surface electromyography (SEMG) can
record the bioelectrical signals on the skin surface quickly and effectively, and thus has been …

Hand movement recognition based on surface electromyography (sEMG) is challenging
because sEMG signals are stochastic, noisy, and difficult to model and have limited …